Layout Translation. As opposed to the original GANformer [1]} that computes \(\text{Attention}(X,W)\)  on-the-fly to determine the assignment between latents and spatial regions, here we rely instead on the pre-planned layout \(S\)  as a conditional guidance source to specify this association. That way, each latent \(w_i\)  modulates the features within the soft region of its respective segment \(s_i\) , achieving a more explicit semantically-meaningful assignment between latents and scene elements. At the same time, since the whole image is still generated together, with all regions processed through the convolution, upsampling and modulation layers of \(G_2\) , the model can still ensure global visual coherence.
